参数优化的图卷积门控循环网络地铁客流预测
DOI:
作者:
作者单位:

1.福建理工大学交通运输学院;2.福建理工大学

作者简介:

通讯作者:

中图分类号:

基金项目:

福建省自然科学(2023J01946)


Parameter optimization of graph convolution gated cyclic network for subway passenger flow prediction
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    【目的】充分挖掘地铁网络相关站点间客流的空间关联性对地铁客流预测精度的提升有积极作用。由于地铁各站点之间的空间相关性难以学习并传递,捕捉并量化客流数据的空间规律十分困难。【方法】提出一种改进的图卷积门控循环神经网络地铁客流预测模型,通过整合多元时空数据提升模型处理不同数据类型的能力。采用基于Tent混沌映射和莱维飞行扰动策略的蜘蛛黄蜂优化算法动态调整模型结构参数,以优化门控循环神经网络的隐层结构。【结果】在工作日和周末的预测结果表明,迭代20次可以达到最佳效果,在训练样本较多的情况下,预测精确度更高。【结论】动态优化门控循环神经网络的隐层结构可以获得更好的收敛效果,预测精确度更高。

    Abstract:

    【Objective】Fully exploiting the spatial correlation of passenger flow between related stations in the subway network has a positive effect on the improvement of subway passenger flow prediction accuracy. Capturing and quantifying spatial patterns in passenger flow data is difficult due to the difficulty of learning and transferring spatial correlations between metro stations. 【Method】An improved graph-convolution gated recurrent neural network metro passenger flow prediction model is proposed to enhance the model"s ability to handle different data types by integrating multivariate spatio-temporal data. A spider wasp optimisation algorithm based on Tent chaotic mapping and Levi"s flight perturbation strategy is used to dynamically adjust the model structural parameters in order to optimise the hidden layer structure of the gated recurrent neural network.【Result】Prediction results on weekdays and weekends show that 20 iterations lead to optimal results, with higher prediction accuracy with larger training samples. 【Conclusion】Dynamic optimisation of the hidden structure of gated recurrent networks can lead to better convergence of the prediction model and higher prediction accuracy.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-03-08
  • 最后修改日期:2024-04-18
  • 录用日期:2024-04-23
  • 在线发布日期:
  • 出版日期: